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An energy-efficient task scheduling method for heterogeneous cloud computing systems using capuchin search and inverted ant colony optimization algorithm

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Abstract

Cloud computing (CC) is a computing paradigm to satisfy end users' computing and storage needs. Cloud data centers (DC) must continuously improve their performance due to the exponential rise in service demand. Task scheduling is an essential part of CC to achieve optimal resource utilization, reduced energy consumption (EC), minimum response time, and maximum efficiency. Scheduling algorithms are crucial for task scheduling and resource mapping in distributed and parallel systems. This study proposes a novel approach for migrating virtual machines (VMs) using a capuchin search algorithm (CapSA). The proposed approach seeks to utilize the strengths of migration and scheduling based on a hybrid multi-objective CapSA and inverted ant colony optimization (IACO) algorithms and selects an optimal algorithm to apply to the succeeding task by adopting a decision-making framework according to the received tasks' conditions. The proposed approach outperforms the earlier approaches regarding EC, execution time (ET), and load balancing by 15–20%.

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We confirm that no datasets were used, generated, or analyzed during the current study.

Abbreviations

\({\mathrm{RPU}}_{i}\) :

CPU efficiency

\({\mathrm{RMU}}_{i}\) :

Memory efficiency

EC:

Energy consumption

C :

Number of memory

\({\mathrm{EC}}_{\mathrm{max}}\) and \({\mathrm{EC}}_{\mathrm{min}}\) :

Maximum and minimum energy consumption

t :

Time

\(N({EC}_{i})\) :

Normalized energy consumption

TET:

Task execution time

\({\mathrm{fit}}_{m}\) :

Fitness function

\({w}_{1}\) and \({w}_{2}\) :

Weight coefficients

\({cpu}_{th}\) :

Threshold value of processor U

\({vc}_{j}\) :

CPU capacity of VMs accumulated on node I

\({vm}_{j}\) :

VMs' memory accumulated on node I

MEM:

Memory capacity threshold of node I

\({m}_{i}\) :

Memory capacity of node I

\(\mathrm{E n e r g yth}\) :

Energy threshold value of node I

\({\mathrm{P}}_{\mathrm{ij}}^{\mathrm{k}}\left(\mathrm{t}\right)\) :

Assign the next task

VM:

Virtual machine

\({\tau }_{ij}(t)\) :

Pheromone corresponding to the vertex or edge connected

\({d}_{ij}^{k}\) :

Distance of the path (imp)

\(\mathrm{TL}\_{\mathrm{Task}}_{\mathrm{i}}\) :

Final length of the task assigned to \({\mathrm{VM}}_{\mathrm{j}}\)

\(\mathrm{Pe}\_{\mathrm{num}}_{j}\) :

Number of \({\mathrm{VM}}_{\mathrm{j}}\) processors

\(\mathrm{Pe}\_{\mathrm{mips}}_{\mathrm{j}}\) :

MIPS (performance) of each CPU in \({\mathrm{VM}}_{\mathrm{j}}\)

\(\mathrm{InputFileSize}\) :

Length of the task is before execution.

\(\mathrm{VM}\_{\mathrm{bw}}_{j}\) :

Communication bandwidth of \({\mathrm{VM}}_{\mathrm{j}}\)

α and β :

Meta-heuristic parameters

\(\Delta {\tau }_{ij}^{k}\left(t\right)\) :

Ant generates and lays a pheromone

\({L}^{k}(t)\) :

Total length of ET obtained

Q:

Adaptive metric

\({d}_{ij}\) :

Set of tasks allocated to \({\mathrm{VM}}_{\mathrm{j}}\)

ρ :

Pheromone trail's evaporation or decay parameter

\({\tau }_{ij}\left(t+1\right)\) :

Pheromone corresponding to each edge is updated

T + :

Best tour based on the pheromone value

L + :

Best tour (T+)

MAD:

(Mean absolute deviation) method to detect under-loaded hosts.

S :

Threshold limit

R = {\({r}_{i}\)| 1 ≤ i ≤ M}:

Set of M nodes

VM = {\({v}_{j}\)| 1 ≤ j ≤ N}:

Set of N VM

JOB = {\({\mathrm{job}}_{k}\) | 1 ≤ k ≤ L}:

Set of K jobs

\({c}_{i}\) :

CPU capacity of node i

\({m}_{i}\) :

Memory capacity in node i

Distance:

Distance parameter

\(loa{d}_{ij}\) :

Compute each VM's load on the SN

\({\mathrm{job}}_{j}\) :

Total number of jobs in \({\mathrm{VM}}_{j}\)

\(\Delta t\) :

Unit time

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Acknowledgements

The authors would like to thank Islamic Azad University, South Tehran Branch, Tehran, Iran, for the support of this project.

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Authors and Affiliations

  1. Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Safdar Rostami, Ali Broumandnia & Ahmad Khademzadeh

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  1. Safdar Rostami
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SR was involved in conceptualization, methodology, writing, validation, and review and editing. AB was involved in conceptualization, supervision, methodology, validation, and review and editing. AK was involved in conceptualization, methodology, validation, and review and editing.

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Correspondence to Ali Broumandnia.

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Rostami, S., Broumandnia, A. & Khademzadeh, A. An energy-efficient task scheduling method for heterogeneous cloud computing systems using capuchin search and inverted ant colony optimization algorithm. J Supercomput 80, 7812–7848 (2024). https://doi.org/10.1007/s11227-023-05725-y

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